import datasets
import pyarrow

def test_local_hf_match(dataset_tag):
    print(f"For dataset : '{dataset_tag}' testing if local and remote ids match ...")
    ids_hf = datasets.load_dataset(
        path = "RosettaCommons/MIP",
        name = dataset_tag,
        data_dir = dataset_tag,
        cache_dir = "/scratch/maom_root/maom0/maom",
        keep_in_memory = True).data['train'].select(['id']).to_pandas()
    ids_local = pyarrow.parquet.read_table(
        source = f"intermediate/{dataset_tag}.parquet",
        columns = ["id"]).to_pandas()
    assert ids_local.equals(ids_hf)
    

test_local_hf_match("rosetta_high_quality_models")
test_local_hf_match("rosetta_low_quality_models")
test_local_hf_match("dmpfold_high_quality_models")
test_local_hf_match("dmpfold_low_quality_models")

test_local_hf_match("rosetta_high_quality_function_predictions")
test_local_hf_match("rosetta_low_quality_function_predictions")
test_local_hf_match("dmpfold_high_quality_function_predictions")
test_local_hf_match("dmpfold_low_quality_function_predictions")



import pandas
dataset_long = pyarrow.parquet.read_table(
    "intermediate/dmpfold_low_quality_function_predictions.parquet").to_pandas()

dataset_wide = pandas.pivot(
    dataset_long[["id", "term_id", "Y_hat"]],
    columns = "term_id",
    index = "id",
    values = "Y_hat")